BOREAS RSS-08 Snow Maps Derived from Landsat TM Imagery Summary: The BOREAS RSS-08 team utilized Landsat TM images to perform mapping of snow extent over the SSA. This data set consists of two Landsat TM images which were used to determine the snow-covered pixels over the BOREAS SSA on 18-Jan-1993 and on 06-Feb-1994. The data are stored in binary image format files. Table of Contents 1. Data Set Overview 2. Investigators 3. Theory of Measurements 4. Equipment 5. Data Acquisition Methods 6. Observations 7. Data Description 8. Data Organization 9. Data Manipulations 10. Error 11. Notes 12. Application of the Data Set 13. Future Modifications and Plans 14. Software 15. Data Access 16. Output Products and Availability 17. References 18. Glossary of Terms 19. List of Acronyms 20. Document Information 1. Data Set Overview 1.1 Data Set Identification BOREAS RSS-08 Snow Maps Derived from Landsat TM Imagery 1.2 Data Set Introduction The Landsat Thematic Mapper (TM) sensor was used to create maps of snow-covered pixels within two winter scenes acquired over the BOReal Ecosystem-Atmosphere Study (BOREAS) Southern Study Area (SSA) in 1993 and 1994. An automated technique for detecting snow-covered pixels using TM image-band ratios and reflectance thresholds was used. 1.3 Objective/Purpose The objective of obtaining this data set was to use an automated technique to map snow in the BOREAS SSA. The algorithm selected to do this, called SNOMAP, has been developed to map snow using future Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) data. A secondary objective was to compare the results of snow mapping using the Landsat TM and MODIS Airborne Simulator (MAS) sensors in order to determine the relative accuracy of the snow maps. However, very little MAS data were available for comparison. Although the Landsat TM sensor can be used as a prototype for the MODIS sensor it is not ideal as a prototype for MODIS because it does not have the same spectral bands as MODIS and its Field-Of-View (FOV) angle is only ±8°, while that of the MODIS sensor is ±55°. Nevertheless, until receipt of significant MODIS Airborne Simulator (MAS) data, the TM was the most appropriate prototype sensor available for developing a snow-mapping algorithm for MODIS. For investigators who are using snow maps in their modeling efforts, the SNOMAP- derived snow maps may be useful. 1.4 Summary of Parameters Snow in the TM scene and in the BOREAS SSA test site is the parameter of interest. 1.5 Discussion The experiment was conducted in order to determine the accuracy of snow mapping using the MODIS snow-mapping algorithm, SNOMAP, in different forest types; specifically, deciduous and coniferous forests. Field measurements were acquired simultaneously with the Landsat TM test data. In addition, a National Aeronautics and Space Administration (NASA) ER-2 overflight was flown on 08-Feb- 1994. MAS data were acquired using the MAS when it had only 7 spectral bands available; only two scenes of MAS data were acquired, and clouds contaminated one of the scenes. 1.6 Related Data Sets BOREAS HYD-02 Estimated Snow Water Equivalent (SWE) from Microwave BOREAS Level-2 MAS Imagery: Reflectance and Temperatures in BSQ Format BOREAS Level-3a Landsat TM Imagery: Scaled At-sensor Radiance in BSQ Format 2 Investigators 2.1 Investigators’ Names and Title Principal Investigator: Dorothy K. Hall Scientist Co-Investigators: Alfred T. C. Chang Scientist James L. Foster Scientist 2.2 Title of Investigation Automated Snow Mapping in the Southern BOREAS Test Site 2.3 Contact Information Contact 1 --------- Dorothy K. Hall Hydrological Sciences Branch NASA/GSFC Greenbelt, MD ph: 301-286-6892 fax: 301-286-1758 email: Dorothy.K.Hall@gsfc.nasa.gov Contact 2 ------------- Alfred T. C. Chang Hydrological Sciences Branch NASA/GSFC Greenbelt, MD ph: 286-8997 fax: 301-286-1758 Alfred.T.Chang@gsfc.nasa.gov Contact 3 ------------- James L. Foster Hydrological Sciences Branch NASA/GSFC Greenbelt, MD ph: 301-286-7096 fax: 301-286-1758 James.L.Foster@gsfc.nasa.gov 3. Theory of Measurements The normalized-difference snow index (NDSI) is useful for the identification of snow and ice and for separating snow/ice and most cumulus clouds to improve identification of snow/ice and decrease reliance on single-band, "universal" thresholds. The NDSI is a measure of the relative magnitude of the characteristic reflectance difference between the visible and short-wave Infrared (IR) reflectance of snow. The NDSI is insensitive to a wide range of illumination conditions, is partially normalized for atmospheric effects, and does not depend on reflectance in a single band. The NDSI is analogous to the normalized-difference vegetation index (NDVI). Various other techniques employing band ratio techniques have been used previously to map snow, as discussed in Section 1. For Landsat TM data, the NDSI is calculated as: NDSI = (TM Band 2 - TM Band 5) /(TM Band 2 + TM Band 5) Pixels with 50% or greater snow coverage have been found to have NDSI values greater than or equal to 0.4. Separation of snow and water is done by a TM band 4 reflectance test. If the reflectance of TM band 4 is greater than 11%, and the NDSI is greater than or equal to 0.40, snow covers 50% or more of the pixel. The NDSI threshold has been determined from detailed analysis of numerous TM scenes and comparisons with supervised-classification techniques. 4. Equipment 4.1 Sensor/Instrument Description The TM sensor system records radiation from seven bands in the electromagnetic spectrum. It has a telescope that directs the incoming radiant flux obtained along a scan line through a scan line collector to the visible and near-infrared focal plane, or to the mid-infrared and thermal infrared cooled focal plane. The detectors for the visible and near-infrared bands (1 to 4) are four staggered linear arrays, each containing 16 silicon detectors. The two mid-infrared detectors are 16 indium-antimonide cells in a staggered linear array, and the thermal-infrared detector is a four-element array of mercury-cadmium-telluride cells. The spectral regions, band widths, and primary use of each channel are given in the following table: Channel Wavelength (µm) Primary Use ------- --------------- ---------------------------------------------------- 1 0.451 - 0.521 Coastal water mapping, soil vegetation differentiation, deciduous/coniferous differentiation. 2 0.526 - 0.615 Green reflectance by healthy vegetation. 3 0.622 - 0.699 Chlorophyll absorption for plant species differentiation. 4 0.771 - 0.905 Biomass surveys, water body delineation. 5 1.564 - 1.790 Vegetation moisture measurement, snow cloud differentiation. 6 10.450 - 12.46 Plant heat stress measurement, other thermal mapping. 7 2.083 - 2.351 Hydrothermal mapping. 4.1.1 Collection Environment Data were collected on 06-Feb-1994 when the temperatures were very cold (approximately -20 °F). There was generally some cloud cover. The ground was continuously snow covered except for tree branches, stems, and canopies. Coniferous tree canopies were typically snow free, although there was some snow in the canopies. No ground measurements were available for the January 1993 scene. The BOREAS Landsat TM level-3s and -3p images were acquired through the Canada Centre for Remote Sensing (CCRS). Radiometric corrections and systematic geometric corrections are applied to produce the images in a path-oriented, systematically corrected (level-3s) or precision-corrected (level-3p) form. A full TM image contains 6,920 pixels in each of 5,728 lines. Before any geometric corrections, the ground resolution is 30 m for bands 1, 2, 3, 4, 5, and 7 and 120 m for band 6 at nadir. The pixel values of the images can range from 0 to 255. This allows each pixel to be stored in a single byte field. The level-3s and level-3p images were processed through the CCRS Geocoded Image Correction System (GICS). 4.1.3 Source/Platform Mission Objectives The Landsat TM is designed to respond to and measure both reflected and emitted Earth surface radiation to enable the investigation, survey, inventory, and mapping of Earth's natural resources. 4.1.4 Key Variables Reflected radiation, emitted radiation, temperature. 4.1.5 Principles of Operation The TM is a scanning optical sensor operating in the visible and infrared wavelengths. It contains a scan mirror assembly that directly projects the reflected Earth radiation onto detectors arrayed in two focal planes. The TM achieves better image resolution, sharper color separation, and greater in- flight geometric and radiometric accuracy for seven spectral bands simultaneously than the previous generation sensor, the Multi-Spectral Scanner (MSS). Data collected by the sensor are beamed back to ground receiving stations for processing. 4.1.6 Sensor/Instrument Measurement Geometry The TM sensor depends on the forward motion of the spacecraft for the along- track scan and uses a moving mirror assembly to scan in the cross-track direction (perpendicular to the spacecraft). The instantaneous field-of-view (IFOV) for each detector from bands 1-5 and band 7 is equivalent to a 30-m square when projected to the ground at nadir; band 6 (the thermal infrared band) has an IFOV equivalent to a 120-m square at nadir. 4.1.7 Manufacturer of Sensor/Instrument NASA/GSFC Greenbelt, MD 20771 Hughes Aircraft Company Santa Barbara Remote Sensing (SBRS) 75 Coromar Drive Goleta, CA 93117 4.2 Calibration The internal calibrator, a flex-pivot-mounted shutter assembly, is synchronized with the scan mirror, oscillating at the same 7-Hz frequency. During the turnaround period of the scan mirror, the shutter introduces the calibration source energy and a black direct-current restoration surface into the 100 detector FOV. The calibration signals for bands 1-5 and band 7 are derived from three regulated tungsten-filament lamps. The calibration source for band 6 is a blackbody with three temperature selections, commanded from the ground. The method for transmitting radiation to the moving calibration shutter allows the tungsten lamps to provide radiation independently and to contribute proportionately to the illumination of all detectors. 4.2.1 Specifications Radiometric Band Sensitivity [NE(dP)]* ---- -------------------- 1 0.8% 2 0.5% 3 0.5% 4 0.5% 5 1.0% 6 0.5 K [NE(dT)] 7 2.4% Ground IFOV 30 m (Bands 1-5, 7) 120 m (Band 6) Avg. altitude 699.6 Km Data rate 85 Mbps Quantization levels 256 Orbit angle 8.15° Orbital nodal Period 98.88 minutes Scan width 185 km Scan angle 14.9° Image overlap 7.6% Note: The radiometric sensitivities are the noise-equivalent (NE) reflectance differences for the reflective channels expressed as percentages [NE(dP)], and temperature differences for the thermal infrared bands [NE(dT)] in Kelvin. 4.2.1.1 Tolerance The TM channels were designed for an NE differential represented by the radiometric sensitivity shown in Section 4.2.1. 4.2.2 Frequency of Calibration The absolute radiometric calibration between bands on the TM sensor is maintained by using internal calibrators that are physically located between the telescope and the detectors and are sampled at the end of a scan. 4.2.3 Other Calibration Information Relative within-band radiometric calibration, to reduce "striping," is provided by a scene-based procedure called histogram equalization. The absolute accuracy and relative precision of this calibration scheme assumes that any changes in the optics of the primary telescope or the "effective radiance" from the internal calibrator lamps are insignificant in comparison to the changes in detector sensitivity and electronic gain and bias with time, and that the scene- dependent sampling is sufficiently precise for the required within-scan destriping from histogram equalization. Each TM reflective band and the internal calibrator lamps were calibrated prior to launch using lamps in integrating spheres that in turn were calibrated against lamps traceable to calibrated National Bureau of Standards lamps. The absolute radiometric calibration constants in the "short-term" and "long-term" parameter files used for ground processing were modified after launch only when inconsistency existed within or between bands, changes occurred in the inherent dynamic range of the sensors, or it was desirable to make quantized and calibrated values from one sensor match those from another. 5. Data Acquisition Methods The BOREAS Landsat TM level-3s and -3p images were acquired through the CCRS. Radiometric corrections and systematic or precision geometric corrections are applied to produce the images in a path-oriented form. A full TM image contains 6,920 pixels in each of 5,728 lines. Before any geometric corrections, the ground resolution is 30 m for bands 1-5 and band 7 and 120 m for band 6 at nadir. The pixel values of the images can range from 0 to 255. This allows each pixel to be stored in a single-byte field. 6. Observations 6.1 Data Notes None. 6.2 Field Notes During the Landsat TM overflight that occurred on 06-Feb-1994, a team of scientists was on the ground in Prince Albert National Park (PANP), Saskatchewan. Observations indicated that the ground was continuously snow covered except for tree stems, canopies, and trunks, which were largely snow free. Some snow, but not very much, was present on the coniferous tree canopy. 7. Data Description 7.1 Spatial Characteristics 7.1.1 Spatial Coverage The BOREAS level-3a Landsat TM images used for these products were World Reference System (WRS) path/row 37/22-23 and cover the entire SSA and more. The North American Datum of 1983 (NAD83) corner coordinates of the SSA are: Latitude Longitude -------- --------- Northwest 54.321 N 106.228 W Northeast 54.225 N 104.237 W Southwest 53.515 N 106.321 W Southeast 53.420 N 104.368 W The NAD83 nominal scene corner coordinates of the scenes used were: Latitude Longitude -------- --------- Northwest 54.78147N 106.23289W Northeast 54.37192N 103.44300W Southwest 53.28872N 106.91269W Southeast 52.89536N 104.21175W 7.1.2 Spatial Coverage Map Not available. 7.1.3 Spatial Resolution The images derived here have the same spatial resolution as the Landsat TM level-3a product. The level-3s and -3p Landsat TM images have had geometric corrections applied so that the spatial resolution for all pixels is 30 m in all bands. 7.1.4 Projection The two TM scenes processed for this data set were registered to each other. The 06-Feb-1994 scene was used as the reference scene in this case. These products, like the level-3a Landsat TM image is in a Universal Transverse Mercator (UTM) projection based on the NAD83. 7.1.5 Grid Description The pixel/grid spacing for each pixel in these images is 30 m in the UTM projection. 7.2 Temporal Characteristics 7.2.1 Temporal Coverage The data submitted are snow maps derived from the 18-Jan-1993 and 06-Feb-1994 TM scenes. 7.2.2 Temporal Coverage Map Not available. 7.2.3 Temporal Resolution Two Landsat TM winter scenes from 1993 and 1994 were selected to create these snow map products. 7.3 Data Characteristics 7.3.1 Parameter/Variable Snow cover. 7.3.2 Variable Description/Definition A snow-covered pixel. 7.3.3 Unit of Measurement Coded but unitless value. 7.3.4 Data Source The level-3a Landsat TM images used to create the snow maps were supplied to BOREAS by CCRS. 7.3.5 Data Range The derived map is binary. Each pixel is considered to be either snow covered or not snow covered. If a pixel is approximately 50% snow covered, it will be mapped as snow. Snow-covered pixels are mapped to a Digital Number (DN) of 1, all others are zero. 7.4 Sample Data Record Not applicable to image data. 8. Data Organization 8.1 Data Granularity The smallest unit of this data set tracked by BORIS is each individual snow map image. 8.2 Data Format(s) 8.2.1 Uncompressed Data Files Three files comprise the Landsat TM snow map product, one 80-byte ASCII header file and two binary image files. The image files contain one image line per physical record. Each record has 6,930 bytes (image samples) of image data for each of 5,728 records (image lines) on tape. There are no header records in the image files. 8.2.2 Compressed CD-ROM Files On the BOREAS CD-ROMs, the ASCII header file for this image is stored as ASCII text; however, the image files been compressed with the Gzip (GNU zip) compression program (file_name.gz). These data have been compressed using gzip version 1.2.4 and the high compression (-9) option (Copyright (C) 1992-1993 Jean-loup Gailly). Gzip uses the Lempel-Ziv algorithm (Welch, 1994) also used in the zip and PKZIP programs. The compressed files may be uncompressed using gzip (with the -d option) or gunzip. Gzip is available from many websites (for example, the ftp site prep.ai.mit.edu/pub/gnu/gzip-*.*) for a variety of operating systems in both executable and source code form. Versions of the decompression software for various systems are included on the CD-ROMs. 9. Data Manipulations 9.1 Formulae The following formula is used to map snow cover: If (TM2-TM5)/(TM2+TM5) >= 0.4, and RTM4 >= 11%, then the pixel is snow covered. TM2, TM4, and TM5 are Landsat TM bands 2, 4, and 5, respectively. RTM4 is reflectance of band 4. 9.2 Data Processing Sequence 9.2.1 Processing Steps None other than described above. 9.2.2 Processing Changes None. 9.3 Calculations 9.3.1 Special Corrections/Adjustments None. 9.3.2 Calculated Variables The derived map is binary. Each pixel is considered to be either snow covered or not snow covered based on the formula in Section 9.1 9.4 Graphs and Plots None given. 10. Errors 10.1 Sources of Error There may be errors caused by thin cloud cover being mapped as snow cover, particularly on the 06-Feb-1994 image. In addition, errors could exist where any ground cover was obscured by tree canopies, stems, and trunks. 10.2 Quality Assessment 10.2.1 Data Validation by Source Data have not been validated. 10.2.2 Confidence Level/Accuracy Judgment The level-3a TM image data used were of good quality. The quality of the snow maps, however, has not been ascertained. 10.2.3 Measurement Error for Parameters Percentage of snow mapped in coniferous and deciduous forests has been calculated for the 06-Feb-1994 scene. Approximately 72% of the coniferous forests were mapped as snow covered, while only 14% of the deciduous forests were mapped as snow covered. Though the snow was continuous, areas existed that did not have snow because of tree stems, branches, and trunks. It is not understood why more snow is mapped in the coniferous forests than in the deciduous forests; it may relate to grain-sized differences between scenes. 10.2.4 Additional Quality Assessments None given. 10.2.5 Data Verification by Data Center None. 11. Notes 11.1 Limitations of the Data Data are currently useful only for assessing the ability of the MODIS SNOMAP algorithm to map snow in different forest-cover types. 11.2 Known Problems with the Data None. 11.3 Usage Guidance Cirrus clouds obscure much of the 06-Feb-1994 scene. 11.4 Other Relevant Information None. 12. Application of the Data Set So far, the data set has been used to show that the conventional wisdom that more snow will be mapped in deciduous forests than in coniferous forests is not always true. The opposite of this was found in the case of the 6-Feb-1994 TM image. However, there was no measurable difference in the amount of snow mapped between forest-cover type in the 18-Jan-1993 TM image. 13. Future Modification and Plans The MODIS algorithm is being modified to map more snow in forests than is currently possible. 14. Software 14.1 Software Description Software to calculate snow cover from Landsat TM data was developed in-house and is available upon request. 14.2 Software Access Software can be obtained by contacting Dorothy Hall, Code 974, NASA/GSFC, Dorothy.K.Hall@gsfc.nasa.gov. 15. Data Access 15.1 Contact Information Ms. Beth Nelson NASA GSFC Greenbelt, MD (301) 286-4005 (301) 286-0239 (fax) Elizabeth.Nelson@.gsfc.nasa.gov 15.2 Data Center Identification See Section 15.1. 15.3 Procedures for Obtaining Data Users may place requests by telephone, electronic mail, or fax. 15.4 Data Center Status/Plans The RSS-08 snow maps are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The BOREAS contact at ORNL is: ORNL DAAC User Services Oak Ridge National Laboratory Oak Ridge, TN (423) 241-3952 ornldaac@ornl.gov ornl@eos.nasa.gov 16. Output Products and Availability 16.1 Tape Products The snow maps can be made available on 8-mm or Digital Archive Tape (DAT) media. 16.2 Film Products None. 16.3 Other Products None. 17. References 17.1 Platform/Sensor/Instrument/Data Processing Documentation Welch, T.A. 1984, A Technique for High Performance Data Compression, IEEE Computer, Vol. 17, No. 6, pp. 8 - 19. 17.2 Journal Articles and Study Reports Sellers, P. and F. Hall. 1994. Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1994-3.0, NASA BOREAS Report (EXPLAN 94). Sellers, P. and F. Hall. 1996. Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1996-2.0, NASA BOREAS Report (EXPLAN 96). Sellers, P.and F. Hall. 1997. BOREAS Overview Paper. JGR Special Issue (in press). Sellers, P. and F. Hall, K.F. Huemmrich. 1996. Boreal Ecosystem-Atmosphere Study: 1994 Operations. NASA BOREAS Report (OPS DOC 94). Sellers, P. and F. Hall, K.F. Huemmrich. 1997. Boreal Ecosystem-Atmosphere Study: 1996 Operations. NASA BOREAS Report (OPS DOC 96). Sellers, P., F. Hall, H. Margolis, B. Kelly, D. Baldocchi, G. den Hartog, J. Cihlar, M. G. Ryan, B. Goodison, P. Crill, K.J. Ranson, D. Lettenmaier, and D.E. Wickland. 1995. The boreal ecosystem-atmosphere study (BOREAS): an overview and early results from the 1994 field year. Bulletin of the American Meteorological Society. 76(9):1549-1577. 17.3 Archive/DBMS Usage Documentation None. 18. Glossary of Terms None. 19. List of Acronyms ASCII - American Standard Code for Information Interchange BOREAS - BOReal Ecosystem-Atmosphere Study BORIS - BOREAS Information System BPI - Byte per inch CCRS - Canada Centre for Remote Sensing CCT - Computer Compatible Tape CD-ROM - Compact Disk-Read-Only Memory DAAC - Distributed Active Archive Center DAT - Digital Archive Tape DN - Digital Number EOS - Earth Observing System EOSDIS - EOS Data and Information System FOV - Field Of View GICS - Geocoded Image Correction System GSFC - Goddard Space Flight Center IFOV - Instantaneous Field-of-View MAS - MODIS Airborne Simulator MODIS - Moderate-Resolution Imaging Spectroradiometer MSS - Multispectral Scanner NAD83 - North American Datum of 1983 NASA - National Aeronautics and Space Administration NDSI - Normalized Difference Snow Index NDVI - Normailzed Difference Vegetation Index NE - Noise Equivalent NSA - Northern Study Area ORNL - Oak Ridge National Laboratory PANP - Prince Albert National Park SBSR - Santa Barbara Remote Sensing SSA - Southern Study Area TM - Thematic Mapper URL - Uniform Resource Locator UTM - Universal Transverse Mercator WWW - World Wide Web 20. Document Information 20.1 Document Revision Dates Written: 15-Dec-1996 Last Updated: 30-Jun-1998 20.2 Document Review Dates BORIS Review: 10-Sep-1997 Science Review: 15-Nov-1997 20.3 Document ID 20.4 Citation If this data set is referenced by another investigator, please acknowledge the paper by Hall, et al. 1997 in Section 17.2. 20.5 Document Curator 20.6 Document URL Keywords: ------------ Snow Snow Cover Snow Mapping RSS08_Snow_Maps.doc 07/07/98